In this report, we extract information about published JOSS papers and generate graphics as well as a summary table that can be downloaded and used for further analyses.
suppressPackageStartupMessages({
library(tibble)
library(rcrossref)
library(dplyr)
library(tidyr)
library(ggplot2)
library(lubridate)
library(gh)
library(purrr)
library(jsonlite)
library(DT)
library(plotly)
library(citecorp)
library(readr)
})## Keep track of the source of each column
source_track <- c()
## Determine whether to add a caption with today's date to the (non-interactive) plots
add_date_caption <- TRUE
if (add_date_caption) {
dcap <- lubridate::today()
} else {
dcap <- ""
}## Read archived version of summary data frame, to use for filling in
## information about software repositories (due to limit on API requests)
## Sort by the date when software repo info was last obtained
papers_archive <- readRDS(gzcon(url("https://github.com/openjournals/joss-analytics/blob/gh-pages/joss_submission_analytics.rds?raw=true"))) %>%
dplyr::arrange(!is.na(repo_info_obtained), repo_info_obtained)
## Similarly for citation analysis, to avoid having to pull down the
## same information multiple times
citations_archive <- readr::read_delim(
url("https://github.com/openjournals/joss-analytics/blob/gh-pages/joss_submission_citations.tsv?raw=true"),
col_types = cols(.default = "c"), col_names = TRUE,
delim = "\t")We get the information about published JOSS papers from Crossref, using the rcrossref R package. This package is also used to extract citation counts.
## Fetch JOSS papers from Crossref
## Only 1000 papers at the time can be pulled down
lim <- 1000
papers <- rcrossref::cr_works(filter = c(issn = "2475-9066"),
limit = lim)$data
i <- 1
while (nrow(papers) == i * lim) {
papers <- dplyr::bind_rows(
papers,
rcrossref::cr_works(filter = c(issn = "2475-9066"),
limit = lim, offset = i * lim)$data)
i <- i + 1
}
papers <- papers %>%
dplyr::filter(type == "journal-article")
## A few papers don't have DOIs - generate them from the URL
noaltid <- which(is.na(papers$alternative.id))
papers$alternative.id[noaltid] <- gsub("http://dx.doi.org/", "",
papers$url[noaltid])
## Get citation info from Crossref and merge with paper details
cit <- rcrossref::cr_citation_count(doi = papers$alternative.id)
papers <- papers %>% dplyr::left_join(
cit %>% dplyr::rename(citation_count = count),
by = c("alternative.id" = "doi")
)
## Remove one duplicated paper
papers <- papers %>% dplyr::filter(alternative.id != "10.21105/joss.00688")
source_track <- c(source_track,
structure(rep("crossref", ncol(papers)),
names = colnames(papers)))For each published paper, we use the Whedon API to get information about pre-review and review issue numbers, corresponding software repository etc.
whedon <- list()
p <- 1
a <- jsonlite::fromJSON(
url(paste0("https://joss.theoj.org/papers/published.json?page=", p)),
simplifyDataFrame = FALSE
)
while (length(a) > 0) {
whedon <- c(whedon, a)
p <- p + 1
a <- jsonlite::fromJSON(
url(paste0("https://joss.theoj.org/papers/published.json?page=", p)),
simplifyDataFrame = FALSE
)
}
whedon <- do.call(dplyr::bind_rows, lapply(whedon, function(w) {
data.frame(api_title = w$title,
api_state = w$state,
editor = paste(w$metadata$paper$editor, collapse = ","),
reviewers = paste(w$reviewers, collapse = ","),
nbr_reviewers = length(w$reviewers),
repo_url = w$repository_url,
review_issue_id = w$review_issue_id,
doi = w$doi,
prereview_issue_id = ifelse(!is.null(w$meta_review_issue_id),
w$meta_review_issue_id, NA_integer_),
languages = paste(w$metadata$paper$languages, collapse = ","),
archive_doi = w$metadata$paper$archive_doi)
}))
papers <- papers %>% dplyr::left_join(whedon, by = c("alternative.id" = "doi"))
source_track <- c(source_track,
structure(rep("whedon", length(setdiff(colnames(papers),
names(source_track)))),
names = setdiff(colnames(papers), names(source_track))))From each pre-review and review issue, we extract information about review times and assigned labels.
## Pull down info on all issues in the joss-reviews repository
issues <- gh("/repos/openjournals/joss-reviews/issues",
.limit = 5000, state = "all")## From each issue, extract required information
iss <- do.call(dplyr::bind_rows, lapply(issues, function(i) {
data.frame(title = i$title,
number = i$number,
state = i$state,
opened = i$created_at,
closed = ifelse(!is.null(i$closed_at),
i$closed_at, NA_character_),
ncomments = i$comments,
labels = paste(setdiff(
vapply(i$labels, getElement,
name = "name", character(1L)),
c("review", "pre-review", "query-scope", "paused")),
collapse = ","))
}))
## Split into REVIEW, PRE-REVIEW, and other issues (the latter category
## is discarded)
issother <- iss %>% dplyr::filter(!grepl("\\[PRE REVIEW\\]", title) &
!grepl("\\[REVIEW\\]", title))
dim(issother)## [1] 124 7
## title
## 1 Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
## 2 Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
## 3 Installation: Does installation proceed as outlined in the documentation?
## 4 Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
## 5 References to arXiv
## 6 References to arXiv
## number state opened closed ncomments labels
## 1 4150 closed 2022-02-10T19:53:34Z 2022-02-10T19:53:37Z 1
## 2 4141 closed 2022-02-08T21:42:46Z 2022-02-08T21:42:49Z 1
## 3 4134 closed 2022-02-04T19:53:31Z 2022-02-04T19:53:34Z 3
## 4 4128 closed 2022-02-02T23:52:01Z 2022-02-02T23:52:05Z 1
## 5 4122 closed 2022-02-01T23:23:03Z 2022-02-01T23:23:06Z 2
## 6 4121 closed 2022-02-01T23:20:28Z 2022-02-01T23:20:31Z 1
## For REVIEW issues, generate the DOI of the paper from the issue number
getnbrzeros <- function(s) {
paste(rep(0, 5 - nchar(s)), collapse = "")
}
issrev <- iss %>% dplyr::filter(grepl("\\[REVIEW\\]", title)) %>%
dplyr::mutate(nbrzeros = purrr::map_chr(number, getnbrzeros)) %>%
dplyr::mutate(alternative.id = paste0("10.21105/joss.",
nbrzeros,
number)) %>%
dplyr::select(-nbrzeros) %>%
dplyr::mutate(title = gsub("\\[REVIEW\\]: ", "", title)) %>%
dplyr::rename_at(vars(-alternative.id), ~ paste0("review_", .))## For pre-review and review issues, respectively, get the number of
## issues closed each month, and the number of those that have the
## 'rejected' label
review_rejected <- iss %>%
dplyr::filter(grepl("\\[REVIEW\\]", title)) %>%
dplyr::filter(!is.na(closed)) %>%
dplyr::mutate(closedmonth = lubridate::floor_date(as.Date(closed), "month")) %>%
dplyr::group_by(closedmonth) %>%
dplyr::summarize(nbr_issues_closed = length(labels),
nbr_rejections = sum(grepl("rejected", labels))) %>%
dplyr::mutate(itype = "review")
prereview_rejected <- iss %>%
dplyr::filter(grepl("\\[PRE REVIEW\\]", title)) %>%
dplyr::filter(!is.na(closed)) %>%
dplyr::mutate(closedmonth = lubridate::floor_date(as.Date(closed), "month")) %>%
dplyr::group_by(closedmonth) %>%
dplyr::summarize(nbr_issues_closed = length(labels),
nbr_rejections = sum(grepl("rejected", labels))) %>%
dplyr::mutate(itype = "pre-review")
all_rejected <- dplyr::bind_rows(review_rejected, prereview_rejected)## For PRE-REVIEW issues, add information about the corresponding REVIEW
## issue number
isspre <- iss %>% dplyr::filter(grepl("\\[PRE REVIEW\\]", title)) %>%
dplyr::filter(!grepl("withdrawn", labels)) %>%
dplyr::filter(!grepl("rejected", labels))
## Some titles have multiple pre-review issues. In these cases, keep the latest
isspre <- isspre %>% dplyr::arrange(desc(number)) %>%
dplyr::filter(!duplicated(title)) %>%
dplyr::mutate(title = gsub("\\[PRE REVIEW\\]: ", "", title)) %>%
dplyr::rename_all(~ paste0("prerev_", .))
papers <- papers %>% dplyr::left_join(issrev, by = "alternative.id") %>%
dplyr::left_join(isspre, by = c("prereview_issue_id" = "prerev_number")) %>%
dplyr::mutate(prerev_opened = as.Date(prerev_opened),
prerev_closed = as.Date(prerev_closed),
review_opened = as.Date(review_opened),
review_closed = as.Date(review_closed)) %>%
dplyr::mutate(days_in_pre = prerev_closed - prerev_opened,
days_in_rev = review_closed - review_opened,
to_review = !is.na(review_opened))
source_track <- c(source_track,
structure(rep("joss-github", length(setdiff(colnames(papers),
names(source_track)))),
names = setdiff(colnames(papers), names(source_track))))## Reorder so that software repositories that were interrogated longest
## ago are checked first
tmporder <- order(match(papers$alternative.id, papers_archive$alternative.id),
na.last = FALSE)
software_urls <- papers$repo_url[tmporder]
is_github <- grepl("github", software_urls)
length(is_github)## [1] 1548
## [1] 1471
## [1] "https://gitlab.kitware.com/LBM/lattice-boltzmann-solver"
## [2] "https://ts-gitlab.iup.uni-heidelberg.de/dorie/dorie"
## [3] "https://gitlab.inria.fr/bramas/tbfmm"
## [4] "https://ts-gitlab.iup.uni-heidelberg.de/utopia/utopia"
## [5] "https://bitbucket.org/orionmhdteam/orion2_release1/src/master/"
## [6] "https://gitlab.com/myqueue/myqueue"
## [7] "https://bitbucket.org/meg/cbcbeat"
## [8] "https://gitlab.com/fduchate/predihood"
## [9] "https://gitlab.com/mmartin-lagarde/exonoodle-exoplanets/-/tree/master/"
## [10] "https://gitlab.com/pyFBS/pyFBS"
## [11] "https://gitlab.com/ffaucher/hawen"
## [12] "https://gitlab.com/cerfacs/batman"
## [13] "https://bitbucket.org/manuela_s/hcp/"
## [14] "https://gitlab.com/jason-rumengan/pyarma"
## [15] "https://savannah.nongnu.org/projects/complot/"
## [16] "https://gitlab.com/emd-dev/emd"
## [17] "https://gitlab.com/libreumg/dataquier.git"
## [18] "https://bitbucket.org/hammurabicode/hamx"
## [19] "https://gitlab.com/gdetor/genetic_alg"
## [20] "https://ts-gitlab.iup.uni-heidelberg.de/utopia/dantro"
## [21] "http://mutabit.com/repos.fossil/grafoscopio/"
## [22] "https://bitbucket.org/cardosan/brightway2-temporalis"
## [23] "https://gitlab.com/manchester_qbi/manchester_qbi_public/madym_cxx/"
## [24] "https://gitlab.inria.fr/miet/miet"
## [25] "https://gricad-gitlab.univ-grenoble-alpes.fr/ttk/spam/"
## [26] "https://gitlab.ethz.ch/holukas/dyco-dynamic-lag-compensation"
## [27] "https://git.rwth-aachen.de/ants/sensorlab/imea"
## [28] "https://gitlab.com/remram44/taguette"
## [29] "https://bitbucket.org/clhaley/Multitaper.jl"
## [30] "https://earth.bsc.es/gitlab/wuruchi/autosubmitreact"
## [31] "https://gitlab.com/vibes-developers/vibes"
## [32] "https://gitlab.com/marinvaders/marinvaders"
## [33] "https://gitlab.gwdg.de/mpievolbio-it/crbhits"
## [34] "https://bitbucket.org/rram/dvrlib/src/joss/"
## [35] "https://gitlab.com/dlr-dw/ontocode"
## [36] "https://gitlab.com/project-dare/dare-platform"
## [37] "https://gitlab.com/sails-dev/sails"
## [38] "https://framagit.org/GustaveCoste/eldam"
## [39] "https://gitlab.com/QComms/cqptoolkit"
## [40] "https://gitlab.com/cracklet/cracklet.git"
## [41] "https://bitbucket.org/mpi4py/mpi4py-fft"
## [42] "https://gitlab.inria.fr/azais/treex"
## [43] "https://bitbucket.org/basicsums/basicsums"
## [44] "https://bitbucket.org/sciencecapsule/sciencecapsule"
## [45] "https://gitlab.com/eidheim/Simple-Web-Server"
## [46] "https://bitbucket.org/cdegroot/wediff"
## [47] "https://gitlab.com/toposens/public/ros-packages"
## [48] "https://bitbucket.org/glotzer/rowan"
## [49] "https://www.idpoisson.fr/fullswof/"
## [50] "https://bitbucket.org/berkeleylab/esdr-pygdh/"
## [51] "https://gitlab.com/moorepants/skijumpdesign"
## [52] "https://git.iws.uni-stuttgart.de/tools/frackit"
## [53] "https://gitlab.com/cosmograil/PyCS3"
## [54] "https://gitlab.com/LMSAL_HUB/aia_hub/aiapy"
## [55] "https://gitlab.com/dlr-ve/autumn/"
## [56] "https://gitlab.inria.fr/mosaic/bvpy"
## [57] "https://bitbucket.org/ocellarisproject/ocellaris"
## [58] "https://bitbucket.org/miketuri/perl-spice-sim-seus/"
## [59] "https://bitbucket.org/cmutel/brightway2"
## [60] "https://bitbucket.org/likask/mofem-cephas"
## [61] "https://sourceforge.net/p/mcapl/mcapl_code/ci/master/tree/"
## [62] "https://gitlab.com/davidtourigny/dynamic-fba"
## [63] "https://gitlab.com/materials-modeling/wulffpack"
## [64] "https://bitbucket.org/dolfin-adjoint/pyadjoint"
## [65] "https://gitlab.com/tesch1/cppduals"
## [66] "https://doi.org/10.17605/OSF.IO/3DS6A"
## [67] "https://gitlab.com/energyincities/besos/"
## [68] "https://gitlab.com/geekysquirrel/bigx"
## [69] "https://gitlab.com/gims-developers/gims"
## [70] "https://gitlab.com/celliern/scikit-fdiff/"
## [71] "https://gitlab.com/costrouc/pysrim"
## [72] "https://bitbucket.org/dghoshal/frieda"
## [73] "https://bitbucket.org/mituq/muq2.git"
## [74] "https://gitlab.com/datafold-dev/datafold/"
## [75] "https://bitbucket.org/cloopsy/android/"
## [76] "https://c4science.ch/source/tamaas/"
## [77] "https://gitlab.com/ampere2/metalwalls"
df <- do.call(dplyr::bind_rows, lapply(software_urls[is_github], function(u) {
u0 <- gsub("^http://", "https://", gsub("\\.git$", "", gsub("/$", "", u)))
if (grepl("/tree/", u0)) {
u0 <- strsplit(u0, "/tree/")[[1]][1]
}
if (grepl("/blob/", u0)) {
u0 <- strsplit(u0, "/blob/")[[1]][1]
}
info <- try({
gh(gsub("(https://)?(www.)?github.com/", "/repos/", u0))
})
languages <- try({
gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/languages"),
.limit = 500)
})
topics <- try({
gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/topics"),
.accept = "application/vnd.github.mercy-preview+json", .limit = 500)
})
contribs <- try({
gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/contributors"),
.limit = 500)
})
if (!is(info, "try-error") && length(info) > 1) {
if (!is(contribs, "try-error")) {
if (length(contribs) == 0) {
repo_nbr_contribs <- repo_nbr_contribs_2ormore <- NA_integer_
} else {
repo_nbr_contribs <- length(contribs)
repo_nbr_contribs_2ormore <- sum(vapply(contribs, function(x) x$contributions >= 2, NA_integer_))
if (is.na(repo_nbr_contribs_2ormore)) {
print(contribs)
}
}
} else {
repo_nbr_contribs <- repo_nbr_contribs_2ormore <- NA_integer_
}
if (!is(languages, "try-error")) {
if (length(languages) == 0) {
repolang <- ""
} else {
repolang <- paste(paste(names(unlist(languages)),
unlist(languages), sep = ":"), collapse = ",")
}
} else {
repolang <- ""
}
if (!is(topics, "try-error")) {
if (length(topics$names) == 0) {
repotopics <- ""
} else {
repotopics <- paste(unlist(topics$names), collapse = ",")
}
} else {
repotopics <- ""
}
data.frame(repo_url = u,
repo_created = info$created_at,
repo_updated = info$updated_at,
repo_pushed = info$pushed_at,
repo_nbr_stars = info$stargazers_count,
repo_language = ifelse(!is.null(info$language),
info$language, NA_character_),
repo_languages_bytes = repolang,
repo_topics = repotopics,
repo_license = ifelse(!is.null(info$license),
info$license$key, NA_character_),
repo_nbr_contribs = repo_nbr_contribs,
repo_nbr_contribs_2ormore = repo_nbr_contribs_2ormore
)
} else {
NULL
}
})) %>%
dplyr::mutate(repo_created = as.Date(repo_created),
repo_updated = as.Date(repo_updated),
repo_pushed = as.Date(repo_pushed)) %>%
dplyr::distinct() %>%
dplyr::mutate(repo_info_obtained = lubridate::today())
stopifnot(length(unique(df$repo_url)) == length(df$repo_url))
dim(df)
## For papers not in df (i.e., for which we didn't get a valid response
## from the GitHub API query), use information from the archived data frame
dfarchive <- papers_archive %>%
dplyr::select(colnames(df)[colnames(df) %in% colnames(papers_archive)]) %>%
dplyr::filter(!(repo_url %in% df$repo_url))
df <- dplyr::bind_rows(df, dfarchive)
papers <- papers %>% dplyr::left_join(df, by = "repo_url")
source_track <- c(source_track,
structure(rep("sw-github", length(setdiff(colnames(papers),
names(source_track)))),
names = setdiff(colnames(papers), names(source_track))))## Convert publication date to Date format
## Add information about the half year (H1, H2) of publication
## Count number of authors
papers <- papers %>% dplyr::select(-reference, -license, -link) %>%
dplyr::mutate(published.date = as.Date(published.print)) %>%
dplyr::mutate(
halfyear = paste0(year(published.date),
ifelse(month(published.date) <= 6, "H1", "H2"))
) %>% dplyr::mutate(
halfyear = factor(halfyear,
levels = paste0(rep(sort(unique(year(published.date))),
each = 2), c("H1", "H2")))
) %>% dplyr::mutate(nbr_authors = vapply(author, function(a) nrow(a), NA_integer_))
papers <- papers %>% dplyr::distinct()
source_track <- c(source_track,
structure(rep("cleanup", length(setdiff(colnames(papers),
names(source_track)))),
names = setdiff(colnames(papers), names(source_track))))In some cases, fetching information from (e.g.) the GitHub API fails for a subset of the publications. There are also other reasons for missing values (for example, the earliest submissions do not have an associated pre-review issue). The table below lists the number of missing values for each of the variables in the data frame.
ggplot(papers %>%
dplyr::mutate(pubmonth = lubridate::floor_date(published.date, "month")) %>%
dplyr::group_by(pubmonth) %>%
dplyr::summarize(npub = n()),
aes(x = factor(pubmonth), y = npub)) +
geom_bar(stat = "identity") + theme_minimal() +
labs(x = "", y = "Number of published papers per month", caption = dcap) +
theme(axis.title = element_text(size = 15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))ggplot(papers %>%
dplyr::mutate(pubyear = lubridate::year(published.date)) %>%
dplyr::group_by(pubyear) %>%
dplyr::summarize(npub = n()),
aes(x = factor(pubyear), y = npub)) +
geom_bar(stat = "identity") + theme_minimal() +
labs(x = "", y = "Number of published papers per year", caption = dcap) +
theme(axis.title = element_text(size = 15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))The plots below illustrate the fraction of pre-review and review issues closed during each month that have the ‘rejected’ label attached.
ggplot(all_rejected,
aes(x = factor(closedmonth), y = nbr_rejections/nbr_issues_closed)) +
geom_bar(stat = "identity") +
theme_minimal() +
facet_wrap(~ itype, ncol = 1) +
labs(x = "Month of issue closing", y = "Fraction of issues rejected",
caption = dcap) +
theme(axis.title = element_text(size = 15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))Papers with 20 or more citations are grouped in the “>=20” category.
ggplot(papers %>%
dplyr::mutate(citation_count = replace(citation_count,
citation_count >= 20, ">=20")) %>%
dplyr::mutate(citation_count = factor(citation_count,
levels = c(0:20, ">=20"))) %>%
dplyr::group_by(citation_count) %>%
dplyr::tally(),
aes(x = citation_count, y = n)) +
geom_bar(stat = "identity") +
theme_minimal() +
labs(x = "Crossref citation count", y = "Number of publications", caption = dcap)The table below sorts the JOSS papers in decreasing order by the number of citations in Crossref.
DT::datatable(
papers %>%
dplyr::mutate(url = paste0("<a href='", url, "' target='_blank'>",
url,"</a>")) %>%
dplyr::arrange(desc(citation_count)) %>%
dplyr::select(title, url, published.date, citation_count),
escape = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE)
)plotly::ggplotly(
ggplot(papers, aes(x = published.date, y = citation_count, label = title)) +
geom_point(alpha = 0.5) + theme_bw() + scale_y_sqrt() +
geom_smooth() +
labs(x = "Date of publication", y = "Crossref citation count", caption = dcap) +
theme(axis.title = element_text(size = 15)),
tooltip = c("label", "x", "y")
)Here, we plot the citation count for all papers published within each half year, sorted in decreasing order.
ggplot(papers %>% dplyr::group_by(halfyear) %>%
dplyr::arrange(desc(citation_count)) %>%
dplyr::mutate(idx = seq_along(citation_count)),
aes(x = idx, y = citation_count)) +
geom_point(alpha = 0.5) +
facet_wrap(~ halfyear, scales = "free") +
theme_bw() +
labs(x = "Index", y = "Crossref citation count", caption = dcap)In these plots we investigate whether the time a submission spends in the pre-review or review stage has changed over time.
ggplot(papers, aes(x = prerev_opened, y = as.numeric(days_in_pre))) +
geom_point() + geom_smooth() + theme_bw() +
labs(x = "Date of pre-review opening", y = "Number of days in pre-review",
caption = dcap) +
theme(axis.title = element_text(size = 15))ggplot(papers, aes(x = review_opened, y = as.numeric(days_in_rev))) +
geom_point() + geom_smooth() + theme_bw() +
labs(x = "Date of review opening", y = "Number of days in review",
caption = dcap) +
theme(axis.title = element_text(size = 15))Next, we consider the languages used by the submissions, both as reported by Whedon and based on the information encoded in available GitHub repositories (for the latter, we also record the number of bytes of code written in each language). Note that a given submission can use multiple languages.
## Language information from Whedon
sspl <- strsplit(papers$languages, ",")
all_languages <- unique(unlist(sspl))
langs <- do.call(dplyr::bind_rows, lapply(all_languages, function(l) {
data.frame(language = l,
nbr_submissions_Whedon = sum(vapply(sspl, function(v) l %in% v, 0)))
}))
## Language information from GitHub software repos
a <- lapply(strsplit(papers$repo_languages_bytes, ","), function(w) strsplit(w, ":"))
a <- a[sapply(a, length) > 0]
langbytes <- as.data.frame(t(as.data.frame(a))) %>%
setNames(c("language", "bytes")) %>%
dplyr::mutate(bytes = as.numeric(bytes)) %>%
dplyr::filter(!is.na(language)) %>%
dplyr::group_by(language) %>%
dplyr::summarize(nbr_bytes_GitHub = sum(bytes),
nbr_repos_GitHub = length(bytes)) %>%
dplyr::arrange(desc(nbr_bytes_GitHub))
langs <- dplyr::full_join(langs, langbytes, by = "language")ggplot(langs %>% dplyr::arrange(desc(nbr_submissions_Whedon)) %>%
dplyr::filter(nbr_submissions_Whedon > 10) %>%
dplyr::mutate(language = factor(language, levels = language)),
aes(x = language, y = nbr_submissions_Whedon)) +
geom_bar(stat = "identity") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(x = "", y = "Number of submissions", caption = dcap) +
theme(axis.title = element_text(size = 15))DT::datatable(
langs %>% dplyr::arrange(desc(nbr_bytes_GitHub)),
escape = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE)
)ggplot(langs, aes(x = nbr_repos_GitHub, y = nbr_bytes_GitHub)) +
geom_point() + scale_x_log10() + scale_y_log10() + geom_smooth() +
theme_bw() +
labs(x = "Number of repos using the language",
y = "Total number of bytes of code\nwritten in the language",
caption = dcap) +
theme(axis.title = element_text(size = 15))ggplotly(
ggplot(papers, aes(x = citation_count, y = repo_nbr_stars,
label = title)) +
geom_point(alpha = 0.5) + scale_x_sqrt() + scale_y_sqrt() +
theme_bw() +
labs(x = "Crossref citation count", y = "Number of stars, GitHub repo",
caption = dcap) +
theme(axis.title = element_text(size = 15)),
tooltip = c("label", "x", "y")
)ggplot(papers, aes(x = as.numeric(prerev_opened - repo_created))) +
geom_histogram(bins = 50) +
theme_bw() +
labs(x = "Time (days) from repo creation to JOSS pre-review start",
caption = dcap) +
theme(axis.title = element_text(size = 15))ggplot(papers, aes(x = as.numeric(repo_pushed - review_closed))) +
geom_histogram(bins = 50) +
theme_bw() +
labs(x = "Time (days) from closure of JOSS review to most recent commit in repo",
caption = dcap) +
theme(axis.title = element_text(size = 15)) +
facet_wrap(~ year(published.date), scales = "free_y")Submissions associated with rOpenSci and pyOpenSci are not considered here, since they are not explicitly reviewed at JOSS.
ggplot(papers %>%
dplyr::filter(!grepl("rOpenSci|pyOpenSci", prerev_labels)) %>%
dplyr::mutate(year = year(published.date)),
aes(x = nbr_reviewers)) + geom_bar() +
facet_wrap(~ year) + theme_bw() +
labs(x = "Number of reviewers", y = "Number of submissions", caption = dcap)Submissions associated with rOpenSci and pyOpenSci are not considered here, since they are not explicitly reviewed at JOSS.
reviewers <- papers %>%
dplyr::filter(!grepl("rOpenSci|pyOpenSci", prerev_labels)) %>%
dplyr::mutate(year = year(published.date)) %>%
dplyr::select(reviewers, year) %>%
tidyr::separate_rows(reviewers, sep = ",")
## Most active reviewers
DT::datatable(
reviewers %>% dplyr::group_by(reviewers) %>%
dplyr::summarize(nbr_reviews = length(year),
timespan = paste(unique(c(min(year), max(year))),
collapse = " - ")) %>%
dplyr::arrange(desc(nbr_reviews)),
escape = FALSE, rownames = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE)
)ggplot(papers %>%
dplyr::mutate(year = year(published.date),
`r/pyOpenSci` = factor(
grepl("rOpenSci|pyOpenSci", prerev_labels),
levels = c("TRUE", "FALSE"))),
aes(x = editor)) + geom_bar(aes(fill = `r/pyOpenSci`)) +
theme_bw() + facet_wrap(~ year, ncol = 1) +
scale_fill_manual(values = c(`TRUE` = "grey65", `FALSE` = "grey35")) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(x = "Editor", y = "Number of submissions", caption = dcap)all_licenses <- sort(unique(papers$repo_license))
license_levels = c(grep("apache", all_licenses, value = TRUE),
grep("bsd", all_licenses, value = TRUE),
grep("mit", all_licenses, value = TRUE),
grep("gpl", all_licenses, value = TRUE),
grep("mpl", all_licenses, value = TRUE))
license_levels <- c(license_levels, setdiff(all_licenses, license_levels))
ggplot(papers %>%
dplyr::mutate(repo_license = factor(repo_license,
levels = license_levels)),
aes(x = repo_license)) +
geom_bar() +
theme_bw() +
labs(x = "Software license", y = "Number of submissions", caption = dcap) +
theme(axis.title = element_text(size = 15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
facet_wrap(~ year(published.date), scales = "free_y")## For plots below, replace licenses present in less
## than 2.5% of the submissions by 'other'
tbl <- table(papers$repo_license)
to_replace <- names(tbl[tbl <= 0.025 * nrow(papers)])ggplot(papers %>%
dplyr::mutate(year = year(published.date)) %>%
dplyr::mutate(repo_license = replace(repo_license,
repo_license %in% to_replace,
"other")) %>%
dplyr::mutate(year = factor(year),
repo_license = factor(
repo_license,
levels = license_levels[license_levels %in% repo_license]
)) %>%
dplyr::group_by(year, repo_license, .drop = FALSE) %>%
dplyr::count() %>%
dplyr::mutate(year = as.integer(as.character(year))),
aes(x = year, y = n, fill = repo_license)) + geom_area() +
theme_minimal() +
scale_fill_brewer(palette = "Set1", name = "Software\nlicense",
na.value = "grey") +
theme(axis.title = element_text(size = 15)) +
labs(x = "Year", y = "Number of submissions", caption = dcap)ggplot(papers %>%
dplyr::mutate(year = year(published.date)) %>%
dplyr::mutate(repo_license = replace(repo_license,
repo_license %in% to_replace,
"other")) %>%
dplyr::mutate(year = factor(year),
repo_license = factor(
repo_license,
levels = license_levels[license_levels %in% repo_license]
)) %>%
dplyr::group_by(year, repo_license, .drop = FALSE) %>%
dplyr::summarize(n = n()) %>%
dplyr::mutate(freq = n/sum(n)) %>%
dplyr::mutate(year = as.integer(as.character(year))),
aes(x = year, y = freq, fill = repo_license)) + geom_area() +
theme_minimal() +
scale_fill_brewer(palette = "Set1", name = "Software\nlicense",
na.value = "grey") +
theme(axis.title = element_text(size = 15)) +
labs(x = "Year", y = "Fraction of submissions", caption = dcap)a <- unlist(strsplit(papers$repo_topics, ","))
a <- a[!is.na(a)]
topicfreq <- table(a)
colors <- viridis::viridis(100)
set.seed(1234)
wordcloud::wordcloud(
names(topicfreq), sqrt(topicfreq), min.freq = 1, max.words = 300,
random.order = FALSE, rot.per = 0.05, use.r.layout = FALSE,
colors = colors, scale = c(10, 0.1), random.color = TRUE,
ordered.colors = FALSE, vfont = c("serif", "plain")
)Here, we take a more detailed look at the papers that cite JOSS papers, using data from the Open Citations Corpus.
citations <- tryCatch({
citecorp::oc_coci_cites(doi = papers$alternative.id) %>%
dplyr::distinct() %>%
dplyr::mutate(citation_info_obtained = as.character(lubridate::today()))
}, error = function(e) {
NULL
})
dim(citations)## [1] 15123 8
if (!is.null(citations)) {
citations <- citations %>%
dplyr::filter(!(oci %in% citations_archive$oci))
tmpj <- rcrossref::cr_works(dois = unique(citations$citing))$data %>%
dplyr::select(contains("doi"), contains("container.title"), contains("issn"),
contains("type"), contains("publisher"), contains("prefix"))
citations <- citations %>% dplyr::left_join(tmpj, by = c("citing" = "doi"))
## bioRxiv preprints don't have a 'container.title' or 'issn', but we'll assume
## that they can be
## identified from the prefix 10.1101 - set the container.title
## for these records manually; we may or may not want to count these
## (would it count citations twice, both preprint and publication?)
citations$container.title[citations$prefix == "10.1101"] <- "bioRxiv"
## JOSS is represented by 'The Journal of Open Source Software' as well as
## 'Journal of Open Source Software'
citations$container.title[citations$container.title ==
"Journal of Open Source Software"] <-
"The Journal of Open Source Software"
## Remove real self citations (cited DOI = citing DOI)
citations <- citations %>% dplyr::filter(cited != citing)
## Merge with the archive
citations <- dplyr::bind_rows(citations, citations_archive)
} else {
citations <- citations_archive
if (is.null(citations[["citation_info_obtained"]])) {
citations$citation_info_obtained <- NA_character_
}
}
citations$citation_info_obtained[is.na(citations$citation_info_obtained)] <-
"2021-08-11"
write.table(citations, file = "joss_submission_citations.tsv",
row.names = FALSE, col.names = TRUE, sep = "\t", quote = FALSE)## [1] "2022-02-09"
## Number of JOSS papers with >0 citations included in this collection
length(unique(citations$cited))## [1] 920
## Number of JOSS papers with >0 citations according to Crossref
length(which(papers$citation_count > 0))## [1] 999
## Number of citations from Open Citations Corpus vs Crossref
df0 <- papers %>% dplyr::select(doi, citation_count) %>%
dplyr::full_join(citations %>% dplyr::group_by(cited) %>%
dplyr::tally() %>%
dplyr::mutate(n = replace(n, is.na(n), 0)),
by = c("doi" = "cited"))## [1] 17561
## [1] 15104
## Ratio of total citation count Open Citations Corpus/Crossref
sum(df0$n, na.rm = TRUE)/sum(df0$citation_count, na.rm = TRUE)## [1] 0.8600877
ggplot(df0, aes(x = citation_count, y = n)) +
geom_abline(slope = 1, intercept = 0) +
geom_point(size = 3, alpha = 0.5) +
labs(x = "Crossref citation count", y = "Open Citations Corpus citation count",
caption = dcap) +
theme_bw()## Zoom in
ggplot(df0, aes(x = citation_count, y = n)) +
geom_abline(slope = 1, intercept = 0) +
geom_point(size = 3, alpha = 0.5) +
labs(x = "Crossref citation count", y = "Open Citations Corpus citation count",
caption = dcap) +
theme_bw() +
coord_cartesian(xlim = c(0, 75), ylim = c(0, 75))## [1] 3650
## [1] 3179
topcit <- citations %>% dplyr::group_by(container.title) %>%
dplyr::summarize(nbr_citations_of_joss_papers = length(cited),
nbr_cited_joss_papers = length(unique(cited)),
nbr_citing_papers = length(unique(citing)),
nbr_selfcitations_of_joss_papers = sum(author_sc == "yes"),
fraction_selfcitations = signif(nbr_selfcitations_of_joss_papers /
nbr_citations_of_joss_papers, digits = 3)) %>%
dplyr::arrange(desc(nbr_cited_joss_papers))
DT::datatable(topcit,
escape = FALSE, rownames = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE))plotly::ggplotly(
ggplot(topcit, aes(x = nbr_citations_of_joss_papers, y = nbr_cited_joss_papers,
label = container.title)) +
geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "grey") +
geom_point(size = 3, alpha = 0.5) +
theme_bw() +
labs(caption = dcap, x = "Number of citations of JOSS papers",
y = "Number of cited JOSS papers")
)plotly::ggplotly(
ggplot(topcit, aes(x = nbr_citations_of_joss_papers, y = nbr_cited_joss_papers,
label = container.title)) +
geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "grey") +
geom_point(size = 3, alpha = 0.5) +
theme_bw() +
coord_cartesian(xlim = c(0, 100), ylim = c(0, 50)) +
labs(caption = dcap, x = "Number of citations of JOSS papers",
y = "Number of cited JOSS papers")
)The tibble object with all data collected above is serialized to a file that can be downloaded and reused.
## alternative.id container.title created deposited
## 1 10.21105/joss.02809 Journal of Open Source Software 2021-03-31 2021-03-31
## 2 10.21105/joss.03705 Journal of Open Source Software 2021-10-27 2021-10-27
## 3 10.21105/joss.00634 Journal of Open Source Software 2019-01-05 2019-11-17
## 4 10.21105/joss.00012 The Journal of Open Source Software 2016-05-16 2017-10-24
## 5 10.21105/joss.00596 Journal of Open Source Software 2018-05-11 2018-05-11
## 6 10.21105/joss.00847 Journal of Open Source Software 2018-09-29 2018-09-29
## published.print doi indexed issn issue issued
## 1 2021-03-31 10.21105/joss.02809 2021-12-11 2475-9066 59 2021-03-31
## 2 2021-10-27 10.21105/joss.03705 2021-12-11 2475-9066 66 2021-10-27
## 3 2019-01-05 10.21105/joss.00634 2021-12-11 2475-9066 33 2019-01-05
## 4 2016-05-16 10.21105/joss.00012 2021-12-11 2475-9066 1 2016-05-16
## 5 2018-05-11 10.21105/joss.00596 2021-12-19 2475-9066 25 2018-05-11
## 6 2018-09-28 10.21105/joss.00847 2021-12-18 2475-9066 29 2018-09-28
## member page prefix publisher score source reference.count
## 1 8722 2809 10.21105 The Open Journal 0 Crossref 27
## 2 8722 3705 10.21105 The Open Journal 0 Crossref 31
## 3 8722 634 10.21105 The Open Journal 0 Crossref 6
## 4 8722 12 10.21105 The Open Journal 0 Crossref 4
## 5 8722 596 10.21105 The Open Journal 0 Crossref 5
## 6 8722 847 10.21105 The Open Journal 0 Crossref 9
## references.count is.referenced.by.count
## 1 27 1
## 2 31 0
## 3 6 0
## 4 4 4
## 5 5 2
## 6 9 2
## title
## 1 dymos: A Python package for optimal control of multidisciplinary systems
## 2 serp: An R package for smoothing in ordinal regression
## 3 genieR: An R package for inference of demographic history of phylogenies
## 4 mst_clustering: Clustering via Euclidean Minimum Spanning Trees
## 5 Nostril: A nonsense string evaluator written in Python
## 6 sdmbench: R package for benchmarking species distribution models
## type url volume
## 1 journal-article http://dx.doi.org/10.21105/joss.02809 6
## 2 journal-article http://dx.doi.org/10.21105/joss.03705 6
## 3 journal-article http://dx.doi.org/10.21105/joss.00634 4
## 4 journal-article http://dx.doi.org/10.21105/joss.00012 1
## 5 journal-article http://dx.doi.org/10.21105/joss.00596 3
## 6 journal-article http://dx.doi.org/10.21105/joss.00847 3
## short.container.title
## 1 JOSS
## 2 JOSS
## 3 JOSS
## 4 JOSS
## 5 JOSS
## 6 JOSS
## author
## 1 http://orcid.org/0000-0001-9864-4928, http://orcid.org/0000-0002-7506-7360, NA, NA, FALSE, FALSE, NA, NA, Robert, Justin, Kaushik, Ted, Falck, Gray, Ponnapalli, Wright, first, additional, additional, additional
## 2 http://orcid.org/0000-0003-2572-0023, FALSE, Ejike, Ugba, first
## 3 http://orcid.org/0000-0003-3644-7114, http://orcid.org/0000-0003-3653-4592, http://orcid.org/0000-0002-5207-9879, FALSE, FALSE, FALSE, Fei, Bethany, Simon, Xiang, Dearlove, Frost, first, additional, additional
## 4 http://orcid.org/0000-0002-9623-3401, FALSE, Jake, VanderPlas, first
## 5 http://orcid.org/0000-0001-9105-5960, FALSE, Michael, Hucka, first
## 6 http://orcid.org/0000-0001-5068-4234, FALSE, Boyan, Angelov, first
## citation_count
## 1 1
## 2 0
## 3 0
## 4 4
## 5 2
## 6 2
## api_title
## 1 dymos: A Python package for optimal control of multidisciplinary systems
## 2 serp: An R package for smoothing in ordinal regression
## 3 genieR: An R package for inference of demographic history of phylogenies
## 4 mst_clustering: Clustering via Euclidean Minimum Spanning Trees
## 5 Nostril: A nonsense string evaluator written in Python
## 6 sdmbench: R package for benchmarking species distribution models
## api_state editor reviewers nbr_reviewers
## 1 accepted @dpsanders @goerz,@thowell 2
## 2 accepted @Bisaloo @bernardsilenou,@wesleyburr 2
## 3 accepted @pjotrp @pboesu 1
## 4 accepted @arfon @nicoguaro 1
## 5 accepted @jakevdp @desilinguist 1
## 6 accepted @karthik @sckott,@goldingn 2
## repo_url review_issue_id prereview_issue_id
## 1 https://github.com/OpenMDAO/dymos 2809 2648
## 2 https://github.com/ejikeugba/serp 3705 3657
## 3 https://github.com/xiangfstats/GenieR 634 536
## 4 http://github.com/jakevdp/mst_clustering 12 NA
## 5 https://github.com/casics/nostril 596 574
## 6 https://github.com/boyanangelov/sdmbench 847 823
## languages archive_doi
## 1 Python,TeX https://doi.org/10.5281/zenodo.4646412
## 2 TeX,R https://doi.org/10.5281/zenodo.5596864
## 3 R,TeX,C++ https://doi.org/10.5281/zenodo.2532305
## 4 Jupyter Notebook,Makefile,Python,TeX http://dx.doi.org/10.5281/zenodo.50995
## 5 Python,CSS,Shell http://dx.doi.org/10.22002/D1.935
## 6 R,CSS,TeX https://doi.org/10.5281/zenodo.1436376
## review_title
## 1 dymos: A Python package for optimal control of multidisciplinary systems
## 2 serp: An R package for smoothing in ordinal regression
## 3 genieR: An R package for inference of demographic history of phylogenies
## 4 mst_clustering: Clustering via Minimum Spanning Trees
## 5 Nostril: A nonsense string evaluator written in Python
## 6 sdmbench: An R Package for Benchmarking Species Distribution Models
## review_number review_state review_opened review_closed review_ncomments
## 1 2809 closed 2020-11-01 2021-03-31 114
## 2 3705 closed 2021-09-09 2021-10-27 51
## 3 634 closed 2018-03-18 2019-01-05 36
## 4 12 closed 2016-05-05 2016-05-16 18
## 5 596 closed 2018-02-27 2018-05-11 27
## 6 847 closed 2018-07-25 2018-09-29 41
## review_labels
## 1 accepted,TeX,Shell,Python,recommend-accept,published
## 2 accepted,TeX,R,recommend-accept,published
## 3 accepted,recommend-accept,published
## 4 accepted,recommend-accept,published
## 5 accepted,recommend-accept,published
## 6 accepted,recommend-accept,published
## prerev_title
## 1 dymos: A Python package for optimal control of multidisciplinary systems
## 2 serp: An R package for smoothing in ordinal regression
## 3 genieR: An R package for inference of demographic history of phylogenies
## 4 <NA>
## 5 Nostril: A nonsense string evaluator written in Python
## 6 sdmbench: An R Package for Benchmarking Species Distribution Models
## prerev_state prerev_opened prerev_closed prerev_ncomments prerev_labels
## 1 closed 2020-09-08 2020-11-01 44 TeX,Shell,Python
## 2 closed 2021-08-25 2021-09-09 50 TeX,R
## 3 closed 2018-01-16 2018-04-23 18 TeX,R,C++
## 4 <NA> <NA> <NA> NA <NA>
## 5 closed 2018-02-08 2018-02-27 18 Shell,Python,CSS
## 6 closed 2018-07-13 2018-07-25 13 TeX,R,CSS
## days_in_pre days_in_rev to_review repo_created repo_updated repo_pushed
## 1 54 days 150 days TRUE 2018-02-13 2022-01-17 2022-02-02
## 2 15 days 48 days TRUE 2020-12-03 2021-11-10 2021-11-10
## 3 97 days 293 days TRUE 2016-10-03 2019-04-13 2019-03-21
## 4 NA days 11 days TRUE 2015-10-28 2021-09-18 2016-05-16
## 5 19 days 73 days TRUE 2017-11-28 2022-01-21 2021-10-14
## 6 12 days 66 days TRUE 2018-06-22 2021-11-06 2020-12-12
## repo_nbr_stars repo_language
## 1 98 Python
## 2 1 R
## 3 1 R
## 4 69 Jupyter Notebook
## 5 112 Python
## 6 15 R
## repo_languages_bytes
## 1 Python:2773843,TeX:12293
## 2 R:104344
## 3 R:50207,C++:14544,TeX:1942
## 4 Jupyter Notebook:416862,Python:18113,TeX:1418,Makefile:249
## 5 Python:114840,CSS:11554,Shell:3165
## 6 R:68955,TeX:10997,CSS:42
## repo_topics
## 1 nasa,optimal-control,trajectory-optimization,pseudospectral,openmdao,co-design
## 2
## 3
## 4
## 5 identifiers,detector,nonsense,gibberish,source-code,mining-software-repositories,identifier-string,nonsense-string-evaluator,inference,text-processing
## 6 sdm,ecology,machine-learning,benchmarking,r
## repo_license repo_nbr_contribs repo_nbr_contribs_2ormore repo_info_obtained
## 1 apache-2.0 19 12 2022-02-16
## 2 gpl-2.0 2 2 2022-01-19
## 3 mit 2 2 2022-02-16
## 4 bsd-2-clause 1 1 2022-02-02
## 5 lgpl-2.1 1 1 2022-02-02
## 6 mit 1 1 2022-01-19
## published.date halfyear nbr_authors
## 1 2021-03-31 2021H1 4
## 2 2021-10-27 2021H2 1
## 3 2019-01-05 2019H1 3
## 4 2016-05-16 2016H1 1
## 5 2018-05-11 2018H1 1
## 6 2018-09-28 2018H2 1
To read the current version of this file directly from GitHub, use the following code:
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] readr_2.1.2 citecorp_0.3.0 plotly_4.10.0 DT_0.20
## [5] jsonlite_1.7.3 purrr_0.3.4 gh_1.3.0 lubridate_1.8.0
## [9] ggplot2_3.3.5 tidyr_1.2.0 dplyr_1.0.8 rcrossref_1.1.0.99
## [13] tibble_3.1.6
##
## loaded via a namespace (and not attached):
## [1] viridis_0.6.2 httr_1.4.2 sass_0.4.0 splines_4.1.2
## [5] bit64_4.0.5 vroom_1.5.7 viridisLite_0.4.0 bslib_0.3.1
## [9] shiny_1.7.1 highr_0.9 triebeard_0.3.0 urltools_1.7.3
## [13] yaml_2.2.2 lattice_0.20-45 pillar_1.7.0 glue_1.6.1
## [17] digest_0.6.29 RColorBrewer_1.1-2 promises_1.2.0.1 colorspace_2.0-2
## [21] Matrix_1.3-4 htmltools_0.5.2 httpuv_1.6.5 plyr_1.8.6
## [25] pkgconfig_2.0.3 httpcode_0.3.0 xtable_1.8-4 gitcreds_0.1.1
## [29] scales_1.1.1 whisker_0.4 later_1.3.0 tzdb_0.2.0
## [33] mgcv_1.8-38 generics_0.1.2 farver_2.1.0 ellipsis_0.3.2
## [37] withr_2.4.3 lazyeval_0.2.2 cli_3.2.0 magrittr_2.0.2
## [41] crayon_1.5.0 mime_0.12 evaluate_0.14 fansi_1.0.2
## [45] nlme_3.1-153 xml2_1.3.3 tools_4.1.2 data.table_1.14.2
## [49] hms_1.1.1 lifecycle_1.0.1 stringr_1.4.0 munsell_0.5.0
## [53] compiler_4.1.2 jquerylib_0.1.4 rlang_1.0.1 grid_4.1.2
## [57] htmlwidgets_1.5.4 crosstalk_1.2.0 miniUI_0.1.1.1 labeling_0.4.2
## [61] rmarkdown_2.11 gtable_0.3.0 curl_4.3.2 fauxpas_0.5.0
## [65] R6_2.5.1 gridExtra_2.3 knitr_1.37 fastmap_1.1.0
## [69] bit_4.0.4 utf8_1.2.2 stringi_1.7.6 parallel_4.1.2
## [73] crul_1.2.0 Rcpp_1.0.8 vctrs_0.3.8 wordcloud_2.6
## [77] tidyselect_1.1.1 xfun_0.29